CRAN
ncpen 1.0.0
Unified Algorithm for Non-convex Penalized Estimation for Generalized Linear Models
Released Nov 17, 2018 by Dongshin Kim
Dependencies
Rcpp RcppArmadillo 0.9.200.4.0
An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018)
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml
file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>ncpen</artifactId> <version>1.0.0-b1</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:ncpen')
Test Results
This package was last tested against Renjin 0.9.2710 on Nov 19, 2018.
- coef.cv.ncpen-examples
- coef.ncpen-examples
- control.ncpen-examples
- cv.ncpen-examples
- cv.ncpen.reg-examples
- fold.cv.ncpen-examples
- gic.ncpen-examples
- interact.data-examples
- make.ncpen.data-examples
- ncpen-examples
- ncpen.reg-examples
- plot.cv.ncpen-examples
- plot.ncpen-examples
- power.data-examples
- predict.ncpen-examples
- sam.gen.ncpen-examples
- to.indicators-examples
- to.ncpen.x.mat-examples